The efficiency of sampling-based motion planning brings wide application in autonomous mobile robots. Conventional rapidly exploring random tree (RRT) algorithm and its variants have gained great successes, but there are still challenges for the real-time optimal motion planning of mobile robots in dynamic environments. In this paper, based on Bidirectional RRT (Bi-RRT) and the use of an assisting metric (AM), we propose a novel motion planning algorithm, namely Bi-AM-RRT*. Different from the existing RRT-based methods, the AM is introduced in this paper to optimize the performance of robot motion planning in dynamic environments with obstacles. On this basis, the bidirectional search sampling strategy is employed, in order to increase the planning efficiency. Further, we present an improved rewiring method to shorten path lengths. The effectiveness and efficiency of the proposed Bi-AM-RRT* are proved through comparative experiments in different environments. Experimental results show that the Bi-AM-RRT* algorithm can achieve better performance in terms of path length and search time.
翻译:以取样为基础的运动规划效率使自动移动机器人得到广泛应用。常规的迅速探索随机树算法及其变体取得了巨大成功,但在动态环境中移动机器人实时最佳运动规划方面仍然存在挑战。在本文件中,基于双向RRT(Bi-RRT)和辅助性指标(AM)的使用,我们提出了一种新的运动规划算法,即Bi-AM-RRT*。与现有的RRT方法不同,本文采用了AM,以优化机器人在充满障碍的动态环境中的运动规划的性能。在此基础上,采用了双向搜索抽样战略,以提高规划效率。此外,我们提出了改进的双向搜索抽样方法,缩短路径长度。拟议的Bi-AM-RRRT*的效能和效率通过不同环境的比较试验得到证明。实验结果表明,Bi-AM-RRT*算法可以在路径长度和搜索时间方面实现更好的性能。